332 citations · 702 across the 40 of their papers we have counts for
12 papers · 1 filter
Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
Ryo Toda, Hayato Itoh, Masahiro Oda +6
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanni…
Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes
Masahiro Oda, Tomoaki Suito, Yuichiro Hayashi +9
CT image-based diagnosis of the stomach is developed as a new way of diagnostic method. A virtual unfolded (VU) view is suitable for displaying its wall. In this paper, we propose…
Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation
Masahiro Oda, Kiyohito Tanaka, Hirotsugu Takabatake +3
This paper proposes a realistic image generation method for visualization in endoscopic simulation systems. Endoscopic diagnosis and treatment are performed in many hospitals. To r…
Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation
Masahiro Oda, Hayato Itoh, Kiyohito Tanaka +4
We propose a depth estimation method from a single-shot monocular endoscopic image using Lambertian surface translation by domain adaptation and depth estimation using multi-scale…
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
Masahiro Oda, Tong Zheng, Yuichiro Hayashi +5
This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and death…
Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Masahiro Oda, Yuichiro Hayashi, Yoshito Otake +3
This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease…